We recruited fifty participants from Amazon Mechanical Turk (AMT) to evaluate the effectiveness of our proposed method from a human perspective. We generate a set of samples of manipulated images from 12 different sound sources: e.g., laughing, sobbing, nose-blowing, wind noise, underwater bubbling, explosion, and thunderstorms. Source images are sampled from FFHQ [1]}, LSUN [2]}, and LHQ [3]} datasets. In our human study, participants need to complete the following two questionnaires: (i) Naturalness- Which image manipulation result better expresses the target attribute? and (ii) Perceptual Realism- How realistic are the images below? Note that we use the Likert scale ranging from 1 (low realistic) to 5 (high realistic) for perceptual realism, and we compare our manipulation results with StyleCLIP [4]} and work by Lee et al. [5]}. As we summarized in Fig. REF  (b) and (c), our model clearly outperforms other state-of-the-art approaches (StyleCLIP and Lee et al.) in both human evaluations. A large portion of participants (\(58.4\) %) judge our generated images to be better manipulated than the images of other approaches, and our method obtains a score of 4.333 on average in terms of perceptual realism. This may confirm our model generates more perceptually realistic images than the other models.
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